Breast Cancer Stem Cells and Tumor Heterogeneity: Characteristics and Therapeutic Strategies.

Breast cancer is one of the most frequently detected malignancies worldwide. It is responsible for more than 15% of all death cases caused by cancer in women. Breast cancer is a heterogeneous disease representing various histological types, molecular characteristics, and clinical profiles. However, all breast cancers are organized in a hierarchy of heterogeneous cell populations, with a small proportion of cancer stem cells (breast cancer stem cells (BCSCs)) playing a putative role in cancer progression, and they are responsible for therapeutic failure. In different molecular subtypes of breast cancer, they present different characteristics, with specific marker profiles, prognoses, and treatments. Recent efforts have focused on tackling the Wnt, Notch, Hedgehog, PI3K/Akt/mTOR, and HER2 signaling pathways. Developing diagnostics and therapeutic strategies enables more efficient elimination of the tumor mass together with the stem cell population. Thus, the knowledge about appropriate therapeutic methods targeting both "normal" breast cancer cells and breast cancer stem cell subpopulations is crucial for success in cancer elimination.

Assessing Etiologic Heterogeneity for Multinomial Outcome with Two-Phase Outcome-Dependent Sampling Design.

Etiologic heterogeneity occurs when distinct sets of events or exposures give rise to different subtypes of disease. Inference about subtype-specific exposure effects from two-phase outcome-dependent sampling data requires adjustment for both confounding and the sampling design. Common approaches to inference for these effects do not necessarily appropriately adjust for these sources of bias, or allow for formal comparisons of effects across different subtypes. Herein, using inverse probability weighting (IPW) to fit a multinomial model is shown to yield valid inference with this sampling design for subtype-specific exposure effects and contrasts thereof. The IPW approach is compared to common regression-based methods for assessing exposure effect heterogeneity using simulations. The methods are applied to estimate subtype-specific effects of various exposures on breast cancer risk in the Carolina Breast Cancer Study.

Reproductive characteristics, menopausal status, race and ethnicity, and risk of breast cancer subtypes defined by ER, PR and HER2 status: the Breast Cancer Etiology in Minorities study.

BACKGROUND: Associations between reproductive factors and risk of breast cancer differ by subtype defined by joint estrogen receptor (ER), progesterone receptor (PR), and HER2 expression status. Racial and ethnic differences in the incidence of breast cancer subtypes suggest etiologic heterogeneity, yet data are limited because most studies have included non-Hispanic White women only. METHODS: We analyzed harmonized data for 2,794 breast cancer cases and 4,579 controls, of whom 90% self-identified as African American, Asian American or Hispanic. Questionnaire data were pooled from three population-based studies conducted in California and data on tumor characteristics were obtained from the California Cancer Registry. The study sample included 1,530 luminal A (ER-positive and/or PR-positive, HER2-negative), 442 luminal B (ER-positive and/or PR-positive, HER2-positive), 578 triple-negative (TN; ER-negative, PR-negative, HER2-negative), and 244 HER2-enriched (ER-negative, PR-negative, HER2-positive) cases. We used multivariable unconditional logistic regression models to estimate subtype-specific ORs and 95% confidence intervals associated with parity, breast-feeding, and other reproductive characteristics by menopausal status and race and ethnicity. RESULTS: Subtype-specific associations with reproductive factors revealed some notable differences by menopausal status and race and ethnicity. Specifically, higher parity without breast-feeding was associated with higher risk of luminal A and TN subtypes among premenopausal African American women. In contrast, among Asian American and Hispanic women, regardless of menopausal status, higher parity with a breast-feeding history was associated with lower risk of luminal A subtype. Among premenopausal women only, luminal A subtype was associated with older age at first full-term pregnancy (FTP), longer interval between menarche and first FTP, and shorter interval since last FTP, with similar OR estimates across the three racial and ethnic groups. CONCLUSIONS: Subtype-specific associations with reproductive factors overall and by menopausal status, and race and ethnicity, showed some differences, underscoring that understanding etiologic heterogeneity in racially and ethnically diverse study samples is essential. Breast-feeding is likely the only reproductive factor that is potentially modifiable. Targeted efforts to promote and facilitate breast-feeding could help mitigate the adverse effects of higher parity among premenopausal African American women.

NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset.

BACKGROUND AND OBJECTIVE: Breast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained significant traction in recent years to identify biomarkers and classify subtypes in breast cancer. This kind of research idea from part to whole will also be an inevitable trend in future life science research. Deep learning can integrate and analyze multi-omics data to predict cancer subtypes, which can further drive targeted therapies. However, there are few articles leveraging the nature of deep learning for feature selection. Therefore, this paper proposes a Neural Network and Binary grey Wolf Optimization based BReast CAncer bioMarker (NNBGWO-BRCAMarker) discovery framework using multi-omics data to obtain a series of biomarkers for precise classification of breast cancer subtypes. METHODS: NNBGWO-BRCAMarker consists of two phases: in the first phase, relevant genes are selected using the weights obtained from a trained feedforward neural network; in the second phase, the binary grey wolf optimization algorithm is leveraged to further screen the selected genes, resulting in a set of potential breast cancer biomarkers. RESULTS: The SVM classifier with RBF kernel achieved a classification accuracy of 0.9242 ± 0.03 when trained using the 80 biomarkers identified by NNBGWO-BRCAMarker, as evidenced by the experimental results. We conducted a comprehensive gene set analysis, prognostic analysis, and druggability analysis, unveiling 25 druggable genes, 16 enriched pathways strongly linked to specific subtypes of breast cancer, and 8 genes linked to prognostic outcomes. CONCLUSIONS: The proposed framework successfully identified 80 biomarkers from the multi-omics data, enabling accurate classification of breast cancer subtypes. This discovery may offer novel insights for clinicians to pursue in further studies.

Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models.

Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating and comparing predictions from various machine learning (ML) and deep learning (DL) techniques is challenging but crucial for anticancer drug research. We conducted a comprehensive analysis of 15 ML and 10 DL models, including the models released after 2022, and found that support vector machines (SVMs) with feature combination and selection significantly enhance overall performance. DL models, especially convolutional neural networks (CNNs) with light gradient boosting machine (LGBM) based feature selection approaches, demonstrate improved characterization. Assessment using a new test data set (ACP10) identifies ACPred, MLACP 2.0, AI4ACP, mACPred, and AntiCP2.0_AAC as successive optimal predictors, showcasing robust performance. Our review underscores current prediction tool limitations and advocates for an omnidirectional ACP prediction framework to propel ongoing research.

Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression.

Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve the classification of disease subtypes. In this study, we introduce sCClust (sparse canonical correlation analysis with clustering), a technique to combine high-dimensional omics data using sparse canonical correlation analysis (sCCA), such that the correlation between datasets is maximized. This stage is followed by clustering the integrated data in a lower-dimensional space. We apply sCClust to gene expression and DNA methylation data for three cancer genomics datasets from the Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan-Meier plots and hazard ratio analysis on the three types of cancer-GBM (glioblastoma multiform), lung cancer and colon cancer. Comparison with subtypes identified by both single- and multi-omics studies implies improved clinical association. We also perform pathway over-representation analysis in order to identify up-regulated and down-regulated genes as tentative drug targets. The main goal of the paper is twofold: the integration of epigenomic and transcriptomic datasets followed by elucidating subtypes in the latent space. The significance of this study lies in the enhanced categorization of cancer data, which is crucial to precision medicine.

De-Escalated Therapy and Early Treatment of Recurrences in HPV-Associated Head and Neck Cancer: The Potential for Biomarkers to Revolutionize Personalized Therapy.

Human papillomavirus-associated (HPV+) head and neck squamous cell carcinoma (HNSCC) is the most common HPV-associated cancer in the United States, with a rapid increase in incidence over the last two decades. The burden of HPV+ HNSCC is likely to continue to rise, and given the long latency between infection and the development of HPV+ HNSCC, it is estimated that the effect of the HPV vaccine will not be reflected in HNSCC prevalence until 2060. Efforts have begun to decrease morbidity of standard therapies for this disease, and its improved characterization is being leveraged to identify and target molecular vulnerabilities. Companion biomarkers for new therapies will identify responsive tumors. A more basic understanding of two mechanisms of HPV carcinogenesis in the head and neck has identified subtypes of HPV+ HNSCC that correlate with different carcinogenic programs and that identify tumors with good or poor prognosis. Current development of biomarkers that reliably identify these two subtypes, as well as biomarkers that can detect recurrent disease at an earlier time, will have immediate clinical application.

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning.

BACKGROUND: Classifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes. RESULTS: In this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data. CONCLUSIONS: The experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods.

Classifying breast cancer using multi-view graph neural network based on multi-omics data.

Introduction: As the evaluation indices, cancer grading and subtyping have diverse clinical, pathological, and molecular characteristics with prognostic and therapeutic implications. Although researchers have begun to study cancer differentiation and subtype prediction, most of relevant methods are based on traditional machine learning and rely on single omics data. It is necessary to explore a deep learning algorithm that integrates multi-omics data to achieve classification prediction of cancer differentiation and subtypes. Methods: This paper proposes a multi-omics data fusion algorithm based on a multi-view graph neural network (MVGNN) for predicting cancer differentiation and subtype classification. The model framework consists of a graph convolutional network (GCN) module for learning features from different omics data and an attention module for integrating multi-omics data. Three different types of omics data are used. For each type of omics data, feature selection is performed using methods such as the chi-square test and minimum redundancy maximum relevance (mRMR). Weighted patient similarity networks are constructed based on the selected omics features, and GCN is trained using omics features and corresponding similarity networks. Finally, an attention module integrates different types of omics features and performs the final cancer classification prediction. Results: To validate the cancer classification predictive performance of the MVGNN model, we conducted experimental comparisons with traditional machine learning models and currently popular methods based on integrating multi-omics data using 5-fold cross-validation. Additionally, we performed comparative experiments on cancer differentiation and its subtypes based on single omics data, two omics data, and three omics data. Discussion: This paper proposed the MVGNN model and it performed well in cancer classification prediction based on multiple omics data.

Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification.

BACKGROUND: In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately. RESULTS: This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature. CONCLUSIONS: The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.

Comprehensive analysis of disulfidptosis-related genes: a prognosis model construction and tumor microenvironment characterization in clear cell renal cell carcinoma.

BACKGROUND: Disulfidptosis, a form of cell death induced by abnormal intracellular accumulation of disulfides, is a newly recognized variety of cell death. Clear cell renal cell carcinoma (ccRCC) is a usual urological tumor that poses serious health risks. There are few studies of disulfidptosis-related genes (DRGs) in ccRCC so far. METHODS: The expression, transcriptional variants, and prognostic role of DRGs were assessed. Based on DRGs, consensus unsupervised clustering analysis was performed to stratify ccRCC patients into various subtypes and constructed a DRG risk scoring model. Patients were stratified into high or low-risk groups by this model. We focused on assessing the discrepancy in prognosis, TME, chemotherapeutic susceptibility, and landscape of immune between the two risk groups. Finally, we validated the expression and explored the biological function of the risk scoring gene FLRT3 through in vitro experiments. RESULTS: The different subtypes had significantly different gene expression, immune, and prognostic landscapes. In the two risk groups, the high-risk group had higher TME scores, more significant immune cell infiltration, and a higher probability of benefiting from immunotherapy, but had a worse prognosis. There were also remarkable differences in chemotherapeutic susceptibility between the two risk groups. In ccRCC cells, the expression of FLRT3 was shown to be lower and its overexpression caused a decrease in cell proliferation and metastatic capacity. CONCLUSIONS: Starting from disulfidptosis, we established a new risk scoring model which can provide new ideas for doctors to forecast patient survival and determine clinical treatment plans.

Global characterization of RNA editing in genetic regulation of multiple ovarian cancer subtypes.

RNA editing plays an extensive role in the initiation and progression of cancer. However, the overall profile and molecular functions of RNA editing in different ovarian cancer subtypes have not been fully characterized and elucidated. Here, we conducted a study on RNA editing in four cohorts of ovarian cancer subtypes through large-scale parallel reporting and bioinformatics analysis. Our findings revealed that RNA editing patterns exhibit subtype-specific characteristics within cancer subtypes. The expression pattern of ADAR and the number of differential editing sites varied under different conditions. CCOC and EOC exhibited significant editing deficiency, whereas HGSC and MOC displayed significant editing excess. The sites within the turquoise module of the coedited network also revealed their correlation with ovarian cancer. In addition, we identified an average of over 40,000 cis-edQTLs in the four subtypes. Finally, we explored the association between RNA editing and drug response, uncovering several potentially effective editing-drug pairs (EDP) and suggesting the conceivable utility of RNA editing sites as therapeutic targets for cancer treatment. Overall, our comprehensive study has identified and characterized RNA editing events in various subtypes of ovarian cancer, providing a new perspective for ovarian cancer research and facilitating the development of medical interventions and treatments.

Identifying tumor cell-released extracellular vesicles as biomarkers for breast cancer diagnosis by a three-dimensional hydrogel-based electrochemical immunosensor.

Tumor cell-released LC3+ extracellular vesicles (LC3+ EVs) participate in immunosuppression during autophagy and contribute to the occurrence and development of breast cancer. In view of the strong association between the LC3+ EVs and breast cancer, developing an effective strategy for the quantitative detection of LC3+ EVs levels with high sensitivity to identify LC3+ EVs as new biomarkers for accurate diagnosis of breast cancer is crucial, but yet not been reported. Herein, an ultrasensitive electrochemical immunosensor is presented for the quantitative determination of LC3+ EVs using a three-dimensional graphene oxide hydrogel-methylene blue composite as a redox probe, showing a low detection limit and a wide linear range. With this immunosensor, the expression levels of LC3+ EVs in various practical sample groups including different cancer cell lines, the peripheral blood of tumor-bearing mice before and after immunotherapy, and the peripheral blood from breast cancer patients with different subtypes and stages were clearly distinguished. This study demonstrated that LC3+ EVs were superior as biomarkers for the accurate diagnosis of breast cancer compared to traditional biomarkers, particularly for cancer subtype discrimination. This work would provide a new noninvasive detection tool for the early diagnosis and prognosis assessment of breast cancer in clinics.

Lymphovascular invasion is an independent prognostic factor in breast cancer irrespective of axillary node metastasis and molecular subtypes.

Purpose: Lymphovascular invasion (LVI) is a well-known poor prognostic factor for early breast cancer. However, the effect of LVI on breast cancer subtype and node status remains unknown. In this study, we aimed to evaluate the clinical significance of LVI on the recurrence and long-term survival of patients with early breast cancer by comparing groups according to the subtype and node status. Methods: We retrospectively reviewed the medical records of 4554 patients with breast cancer who underwent breast cancer surgery between January 2010 and December 2017. The primary endpoints were disease-free survival (DFS) and overall survival (OS). Univariate and multivariate analyses were performed to identify prognostic factors related to the DFS and OS according to the nodal status and breast cancer subtype. Results: During a follow-up period of 94 months, the median OS and DFS were 92 and 90 months, respectively. The LVI expression rate was 8.4%. LVI had a negative impact on the DFS and OS, regardless of the lymph node status. LVI was associated with higher recurrence and lower survival in the luminal A, human epidermal growth factor receptor 2-positive, and triple-negative breast cancer subtypes. The Cox proportional hazards model showed that LVI was a significant prognostic factor for both DFS and OS. No correlation has been observed between LVI and the Oncotype Dx results in terms of prognostic value in early breast cancer. Conclusion: LVI is an independent poor prognostic factor in patients with early breast cancer, regardless of the node status and molecular subtype. Therefore, the LVI status should be considered when making treatment decisions for patients with early stage breast cancer; however, further prospective studies are warranted.

Randomized singular value decomposition for integrative subtype analysis of 'omics data' using non-negative matrix factorization.

Integration of multiple 'omics datasets for differentiating cancer subtypes is a powerful technic that leverages the consistent and complementary information across multi-omics data. Matrix factorization is a common technique used in integrative clustering for identifying latent subtype structure across multi-omics data. High dimensionality of the omics data and long computation time have been common challenges of clustering methods. In order to address the challenges, we propose randomized singular value decomposition (RSVD) for integrative clustering using Non-negative Matrix Factorization: intNMF-rsvd. The method utilizes RSVD to reduce the dimensionality by projecting the data into eigen vector space with user specified lower rank. Then, clustering analysis is carried out by estimating common basis matrix across the projected multi-omics datasets. The performance of the proposed method was assessed using the simulated datasets and compared with six state-of-the-art integrative clustering methods using real-life datasets from The Cancer Genome Atlas Study. intNMF-rsvd was found working efficiently and competitively as compared to standard intNMF and other multi-omics clustering methods. Most importantly, intNMF-rsvd can handle large number of features and significantly reduce the computation time. The identified subtypes can be utilized for further clinical association studies to understand the etiology of the disease.

Trajectories of body mass index in adulthood and risk of subtypes of postmenopausal breast cancer.

BACKGROUND: Body fatness is a dynamic exposure throughout life. To provide more insight into the association between body mass index (BMI) and postmenopausal breast cancer, we aimed to examine the age at onset, duration, intensity, and trajectories of body fatness in adulthood in relation to risk of breast cancer subtypes. METHODS: Based on self-reported anthropometry in the prospective Norwegian Women and Cancer Study, we calculated the age at onset, duration, and intensity of overweight and obesity using linear mixed-effects models. BMI trajectories in adulthood were modeled using group-based trajectory modeling. We used Cox proportional hazards models to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) for the associations between BMI exposures and breast cancer subtypes in 148,866 postmenopausal women. RESULTS: A total of 7223 incident invasive postmenopausal breast cancer cases occurred during follow-up. Increased overweight duration and age at the onset of overweight or obesity were associated with luminal A-like breast cancer. Significant heterogeneity was observed in the association between age at overweight and overweight duration and the intrinsic-like subtypes (pheterogeneity 0.03). Compared with women who remained at normal weight throughout adulthood, women with a descending BMI trajectory had a reduced risk of luminal A-like breast cancer (HR 0.54, 95% CI 0.33-0.90), whereas women with ascending BMI trajectories were at increased risk (HR 1.09; 95% CI 1.01-1.17 for "Normal-overweight"; HR 1.20; 95% CI 1.07-1.33 for "Normal-obesity"). Overweight duration and weighted cumulative years of overweight and obesity were inversely associated with luminal B-like breast cancer. CONCLUSIONS: In this exploratory analysis, decreasing body fatness from obesity in adulthood was inversely associated with overall, hormone receptor-positive and luminal A-like breast cancer in postmenopausal women. This study highlights the potential health benefits of reducing weight in adulthood and the health risks associated with increasing weight throughout adult life. Moreover, our data provide evidence of intrinsic-like tumor heterogeneity with regard to age at onset and duration of overweight.

Advances in immunotyping of colorectal cancer.

Immunotherapy has transformed treatment for various types of malignancy. However, the benefit of immunotherapy is limited to a minority of patients with mismatch-repair-deficient (dMMR) and microsatellite instability-high (MSI-H) (dMMR-MSI-H) colorectal cancer (CRC). Understanding the complexity and heterogeneity of the tumor immune microenvironment (TIME) and identifying immune-related CRC subtypes will improve antitumor immunotherapy. Here, we review the current status of immunotherapy and typing schemes for CRC. Immune subtypes have been identified based on TIME and prognostic gene signatures that can both partially explain clinical responses to immune checkpoint inhibitors and the prognosis of patients with CRC. Identifying immune subtypes will improve understanding of complex CRC tumor heterogeneity and refine current immunotherapeutic strategies.

Architecture and topologies of gene regulatory networks associated with breast cancer, adjacent normal, and normal tissues.

Most cancer studies employ adjacent normal tissues to tumors (ANTs) as controls, which are not completely normal and represent a pre-cancerous state. However, the regulatory landscape of ANTs compared to tumor and non-tumor-bearing normal tissues is largely unexplored. Among cancers, breast cancer is the most commonly diagnosed cancer and a leading cause of death in women worldwide, with a lack of sufficient treatment regimens for various reasons. Hence, we aimed to gain deeper insights into normal, pre-cancerous, and cancerous regulatory systems of breast tissues towards identifying ANT and subtype-specific candidate genes. For this, we constructed and analyzed eight gene regulatory networks (GRNs), including five subtypes (viz., Basal, Her2, Luminal A, Luminal B, and Normal-Like), one ANT, and two normal tissue networks. Whereas several topological properties of these GRNs enabled us to identify tumor-related features of ANT, escape velocity centrality (EVC+) identified 24 functionally significant common genes, including well-known genes such as E2F1, FOXA1, JUN, BRCA1, GATA3, ERBB2, and ERBB3 across all six tissues including subtypes and ANT. Similarly, the EVC+ also helped us to identify tissue-specific key genes (Basal: 18, Her2: 6, Luminal A: 5, Luminal B: 5, Normal-Like: 2, and ANT: 7). Additionally, differentially correlated switching gene pairs along with functional, pathway, and disease annotations highlighted the cancer-associated role of these genes. In a nutshell, the present study revealed ANT and subtype-specific regulatory features and key candidate genes, which can be explored further using in vitro and in vivo experiments for better and effective disease management at an early stage.

Luminal breast cancer identity is determined by loss of glucocorticoid receptor activity.

Glucocorticoid receptor (GR) is a transcription factor that plays a crucial role in cancer biology. In this study, we utilized an in silico-designed GR activity signature to demonstrate that GR relates to the proliferative capacity of numerous primary cancer types. In breast cancer, the GR activity status determines luminal subtype identity and has implications for patient outcomes. We reveal that GR engages with estrogen receptor (ER), leading to redistribution of ER on the chromatin. Notably, GR activation leads to upregulation of the ZBTB16 gene, encoding for a transcriptional repressor, which controls growth in ER-positive breast cancer and associates with prognosis in luminal A patients. In relation to ZBTB16's repressive nature, GR activation leads to epigenetic remodeling and loss of histone acetylation at sites proximal to cancer-driving genes. Based on these findings, epigenetic inhibitors reduce viability of ER-positive breast cancer cells that display absence of GR activity. Our findings provide insights into how GR controls ER-positive breast cancer growth and may have implications for patients' prognostication and provide novel therapeutic candidates for breast cancer treatment.

Extracellular Vesicles in Triple-Negative Breast Cancer: Immune Regulation, Biomarkers, and Immunotherapeutic Potential.

Triple-negative breast cancer (TNBC) is an aggressive subtype accounting for ~10-20% of all human BC and is characterized by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) amplification. Owing to its unique molecular profile and limited targeted therapies, TNBC treatment poses significant challenges. Unlike other BC subtypes, TNBC lacks specific molecular targets, rendering endocrine therapies and HER2-targeted treatments ineffective. The chemotherapeutic regimen is the predominant systemic treatment modality for TNBC in current clinical practice. However, the efficacy of chemotherapy in TNBC is variable, with response rates varying between a wide range of patients, and the emerging resistance further adds to the difficulties. Furthermore, TNBC exhibits a higher mutational burden and is acknowledged as the most immunogenic of all BC subtypes. Consequently, the application of immune checkpoint inhibition has been investigated in TNBC, yielding promising outcomes. Recent evidence identified extracellular vesicles (EVs) as an important contributor in the context of TNBC immunotherapy. In view of the extraordinary ability of EVs to transfer bioactive molecules, such as proteins, lipids, DNA, mRNAs, and small miRNAs, between the cells, EVs are considered a promising diagnostic biomarker and novel drug delivery system among the prospects for immunotherapy. The present review provides an in-depth understanding of how EVs influence TNBC progression, its immune regulation, and their contribution as a predictive biomarker for TNBC. The final part of the review focuses on the recent key advances in immunotherapeutic strategies for better understanding the complex interplay between EVs and the immune system in TNBC and further developing EV-based targeted immunotherapies.